Search Results (359)

Enhancing RAW images acquired under low-light conditions remains a fundamental yet challenging problem in computational photography and image signal processing. Recent deep learning-based approaches have shifted from real paired datasets toward synthetic data generation, where sensor noise is typically simulated through physical modeling. However, most existing methods primarily account for additive noise, neglect multiplicative noise components, and rely on global calibration procedures that fail to capture pixel-level manufacturing variability. Consequently, these methods struggle to faithfully reproduce the complex statistics of real sensor noise. To overcome these limitations, this paper introduces a physically grounded composite noise model that jointly incorporates additive and multiplicative noise components. We further propose a per-pixel noise simulation and calibration strategy, which estimates and synthesizes noise individually for each pixel. This physics-based calibration not only circumvents the constraints of global noise modeling but also captures spatial noise variations arising from microscopic CMOS sensor fabrication differences. Inspired by the recent success of rectified-flow methods in image generation, we integrate our physics-based noise synthesis into a rectified-flow generative framework and present PGRF (Physics-Guided Rectified Flow): a physics-guided rectified-flow framework for low-light RAW image enhancement. PGRF leverages the expressive capacity of rectified flows to model complex data distributions, while physical guidance constrains the generation process toward the desired clean image manifold. To evaluate our method, we constructed the LLID, a dedicated indoor low-light RAW benchmark captured using the Sony A7S II camera. Extensive experiments demonstrate that the proposed framework achieves substantial improvements over state-of-the-art methods in low-light RAW image enhancement. Full article

At present, most oilfields in China have entered the late, high-water-cut stage, commonly facing declining single-well productivity and increasingly pronounced reservoir heterogeneity. Prolonged waterflooding has further exacerbated permeability contrast, yielding complex, hard-to-produce residual-oil distributions. Accordingly, the development of efficient enhanced oil recovery (EOR) technologies has become a strategic priority and an urgent research focus in oil and gas field development. Weak gels, typical non-Newtonian fluids, exhibit both viscous and elastic responses, and their distinctive rheology shows broad application potential for crude oil extraction in porous media. Targeting medium–high-permeability reservoirs with high water cut, this study optimized and evaluated a weak gel system. Experimental results demonstrate that the optimized weak gel system achieves remarkable oil displacement performance. The one-dimensional dual-sandpack flooding tests yielded a total recovery of 72.26%, with the weak gel flooding stage contributing an incremental recovery of 14.52%. In the physical three-dimensional model experiments, the total recovery reached 46.12%, of which the weak gel flooding phase accounted for 16.36%. Through one-dimensional sandpack flow experiments and three-dimensional physical model simulations, the oil displacement mechanisms and synergistic effects of the optimized system in heterogeneous reservoirs were systematically elucidated from macro to micro scales. The optimized system demonstrates integrated synergistic performance during flooding, effectively combining mobility control, displacement, and oil-washing mechanisms. Macroscopically, it effectively strips residual oil in high-permeability zones via viscosity enhancement and viscoelastic effects, efficiently blocks high-permeability channels, diverts flow to medium-permeability regions, and enhances macroscopic sweep efficiency. Microscopically, it mobilizes residual oil via normal stress action and a filamentous transport mechanism, improving oil-washing efficiency and increasing ultimate oil recovery. This study demonstrates the technical feasibility and practical effectiveness of the optimized weak gel system for enhancing oil recovery in heterogeneous reservoirs, providing critical technical support for the efficient development of medium–high-permeability reservoirs with high water cut. Full article

Battery mechanical properties degrade progressively with aging, manifesting as expansion pressure in module-constrained cells. Here, an in situ pressure operating system was developed to replicate the mechanical environment of lithium iron phosphate (LFP) prismatic batteries, enabling long-term monitoring under different loads and temperatures. Coupled with quasi-static compression tests on internal components, stress–strain curves and elasticity moduli were obtained to link microscopic behavior with macroscopic pressure response. Results show that irreversible pressure growth is jointly governed by state of health (SOH) and load: under low-load conditions, irreversible pressure increases nonlinearly with SOH, whereas higher loads yield more linear trends. A multilevel physical model encompassing electrodes, cells, and modules was proposed to explain these behaviors. This model takes into account the influence of external pressure on the modulus of the battery, and indicates that SOH and load influence reversible pressure curves through their effect on modulus. A theoretical method was derived to calculate in-module modulus, confirming its linear correlation with the fluctuation amplitude of reversible pressure. Differential pressure-capacity analysis further demonstrated that characteristic changes in expansion pressure reflect modulus evolution, and deviations from this relationship reveal degradation pathways such as gas generation, solid electrolyte interphase (SEI) growth, or lithium plating. This study establishes pressure signals as mechanistic indicators of modulus evolution and provides a framework for diagnosing mechanical degradation in batteries. Full article

Poyang Lake, China’s largest freshwater lake, is an ecologically significant but increasingly vulnerable system threatened by eutrophication and harmful algal blooms driven by human activities. Phytoplankton organisms, as primary producers and sensitive bioindicators, provide critical insights into these ecological changes; however, comprehensive seasonal assessments remain scarce. This study examined intra-annual phytoplankton dynamics at 15 representative sites, with the objectives of quantifying seasonal and spatial variations in community composition, density, biomass, and diversity, and identifying key environmental drivers. Surface water samples were collected during four seasons. Phytoplankton were identified microscopically, and diversity was quantified using Shannon–Wiener, Pielou’s evenness, and Margalef’s richness indices. Concurrent measurements included water temperature (WT), dissolved oxygen (DO), nutrients (TN, TP, NO₃⁻-N, NO₂⁻-N, NH₄⁺-N), chemical oxygen demand (COD), pH, and transparency. Pearson correlation and redundancy analysis (RDA) were applied to evaluate phytoplankton–environment relationships. A total of 118 phytoplankton species belonging to 7 phyla were identified. Chlorophyta, Cyanobacteria, and Bacillariophyta exhibited the highest species richness. The highest seasonal abundances were observed for Microcystis wesenbergii (0.998) in winter, Aulacoseira granulata var. angustissima (0.780) in spring, and Snowella lacustris (0.520) in autumn, indicating pronounced seasonal shifts in dominant taxa across Poyang Lake. Phytoplankton density and biomass peaked in summer, while diversity indices significantly declined with increasing WT. RDA revealed that WT, DO, TP, and transparency collectively explained 45.7% of the community variation, with DO emerging as the most influential factor. These findings demonstrate that physical drivers, particularly thermal conditions and oxygen availability, exert stronger influences on phytoplankton diversity than nutrients alone, challenging nutrient-centric paradigms. Management should integrate hydrological and oxygen regulation with nutrient control, while long-term monitoring, depth-stratified sampling, and trait-based approaches are recommended to improve predictive models under climate variability. Full article

In response to the problems encountered during the pressure-driven oil recovery process in low-permeability oil reservoirs, such as slow pressure transmission, poor liquid supply, vulnerability of the reservoir to damage, and difficulties in injection and production, in order to achieve the goal of high-quality water injection development, based on the theories of rock mechanics and seepage mechanics, combined with large-scale physical model experiments, acoustic emission crack monitoring, and microscopic scanning technology, an oil reservoir and fracture model was established to conduct a feasibility analysis of pressure-driven assisted pressure reduction and enhanced injection, and it was successfully applied in the exploration and development practice of the Shengli Oilfield. The research shows the following: (1) During the pressure-driven process, the distribution of the fracture network system is relatively limited. In the early stages of the process, there will be minor fractures, but they do not communicate or activate effectively. The improvement of physical properties and pore-throat structure is negligible. As the injection flow rate increases, the effective fracture network system begins to be established, and the range of fluid coverage begins to expand. With the progress of the pressure-driven process, the hydraulic fractures gradually extend, the number of activated original fractures gradually increases, the communication area between hydraulic fractures and original fractures gradually increases, and the reservoir modification effect gradually improves. (2) Based on the compression cracking experiment of large object molds, it is concluded that generating effective micro-cracks and activating them to form efficient diversion channels is the key to pressure flooding injection. Combining the mechanical characteristics of the rock in the target layer to precisely control the injection speed and injection pressure can maximize the fracture network, thereby improving the reservoir to achieve the purpose of pressure reduction and injection increase. (3) Different pressure flooding injection parameters were set for the low-permeability oil reservoirs in the study area to simulate the fracture network expansion. Finally, it was concluded that the optimal injection speed for fracture expansion was 1.2 m³/min and the optimal total injection volume was 20,000 m³. Through research, the mechanism of pressure-driven injection and the extent of reservoir modification caused by this pressure-driven process have been enhanced in terms of understanding. Full article

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

The Maxwell–Boltzmann (MB) distribution is important because it provides the statistical foundation for connecting microscopic particle motion to macroscopic gas properties by statistically describing molecular speeds and energies, making it essential for understanding and predicting the behavior of classical ideal gases. This study advances the statistical modeling of lifetime distributions by developing a comprehensive reliability analysis of the MB distribution under an improved adaptive progressive censoring framework. The proposed scheme strategically enhances experimental flexibility by dynamically adjusting censoring protocols, thereby preserving more information from test samples compared to conventional designs. Maximum likelihood estimation, interval estimation, and Bayesian inference are rigorously derived for the MB parameters, with asymptotic properties established to ensure methodological soundness. To address computational challenges, Markov chain Monte Carlo algorithms are employed for efficient Bayesian implementation. A detailed exploration of reliability measures—including hazard rate, mean residual life, and stress–strength models—demonstrates the MB distribution’s suitability for complex reliability settings. Extensive Monte Carlo simulations validate the efficiency and precision of the proposed inferential procedures, highlighting significant gains over traditional censoring approaches. Finally, the utility of the methodology is showcased through real-world applications to physics and engineering datasets, where the MB distribution coupled with such censoring yields superior predictive performance. This genuine examination is conducted through two datasets (including the failure times of aircraft windshields, capturing degradation under extreme environmental and operational stress, and mechanical component failure times) that represent recurrent challenges in industrial systems. This work contributes a unified statistical framework that broadens the applicability of the Maxwell–Boltzmann model in reliability contexts and provides practitioners with a powerful tool for decision making under censored data environments. Full article

Tight sandstone reservoirs generally exhibit poor physical properties and characterization of microscopic pore structure is crucial for evaluating reservoir quality and fluid flow behavior. Fractal dimension provides an effective means to quantify the complexity and heterogeneity of pore structures in such reservoirs. This study investigates tight sandstone reservoirs of the Kongdian Formation in the Nanpi Slope, Cangdong Sag, using cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI), and constant-rate mercury injection (CRMI) experiments. We establish a full-range fractal model to characterize pore-throat distributions and elucidate the correlation between fractal dimensions and reservoir properties, alongside factors influencing pore-structure heterogeneity. Key findings include that (1) pore types are predominantly residual intergranular pores, intergranular dissolution pores, and clay mineral intercrystalline pores, with throats primarily consisting of sheet-like and curved sheet-like types, exhibiting strong pore-structure heterogeneity; (2) full-range fractal dimensions D₁, D₂ and D₄ effectively characterize the heterogeneity of pore structure, where higher D₁ and D₂ values correlate with increased macro–mega pore and micro-fine throat abundance, respectively, indicating enhanced pore connectivity and superior flow capacity, while elevated D₄ reflects greater nano throat complexity, degrading reservoir properties and impeding hydrocarbon flow; (3) compared to conventional methods splicing HPMI and CRMI data at 0.12 μm, the fractal-derived integration point more accurately resolves full-range pore-throat distributions, revealing significant disparities in pore-throat size populations; (4) the fractal dimensions D₁, D₂, and D₄ are collectively governed by clay mineral content, average throat radius, displacement pressure, and tortuosity. Full article

A multiscale thermodynamic model is considered, in which cosmological dynamics enforce persistent non-equilibrium conditions through recursive energy exchange across hierarchically ordered subsystems. The internal energy of each subsystem is recursively determined by energetic interactions with its subcomponents, forming a nested hierarchy extending up to cosmological scales. The total energy of the universe is assumed to be constant, imposing global consistency conditions on local dynamics. On the quantum scale, subsystems remain thermodynamically constrained in their accessible state space due to the unresolved energetic embedding imposed by higher-order couplings. As a result, quantum behavior is interpreted as an effective projection of unresolved thermodynamic interactions. In this view, the wave function serves as a mathematical representation of a subsystem’s thermodynamic embedding, summarizing the unresolved energetic couplings with its environment, as shaped by recursive interactions across cosmological and microscopic scales. Phenomena such as zero-point energy and vacuum fluctuations are thereby understood as residual effects of structural energy constraints. Classical mechanics arises as a limiting case under full energetic resolution, while the quantum formalism reflects thermodynamic incompleteness. This formulation bridges statistical mechanics and quantum theory without metaphysical assumptions. It remains fully compatible with standard formalism, offering a thermodynamic interpretation based solely on energy conservation and hierarchical organization. All effects arise from scale-dependent resolution, not from violations of established physics. Full article

Compressor blades are subjected to multiaxial loads during operation. Using uniaxial life prediction formulas to predict their fatigue life can result in significant errors. Therefore, by analyzing the loading conditions of the blades, a fatigue life prediction model suitable for compressor blades was developed. This model was established by applying the load of a specific engine type to a notched bar specimen and considering the gradient and strengthening effects. Firstly, the parameters of the SWT model were used as the damage parameters to determine the critical plane location based on the principle of coordinate transformation, and these results were compared with the actual fracture angles. Additionally, the physical mechanisms of multiaxial fatigue crack initiation and propagation were investigated at the microscopic level. Secondly, the non-uniform stress field on the critical plane was obtained using the finite element method. The stress distribution from the critical point to the specimen’s principal axis was extracted and normalized to calculate the equivalent stress gradient factor. Finally, the results of the comprehensive fatigue life prediction model were computed. Comparisons between the calculated results of the proposed model, the SWT model, and the Shang model with the experimental fatigue life showed that the prediction accuracy of the proposed model is higher than that of the SWT model and the Shang Deguang model. Full article

Tight sandstone gas is currently one of the largest unconventional oil and gas resources being developed. In actual reservoir development, the complex pore structure affects the distribution of residual gas and water during the displacement process. However, there is still a lack of experimental research on the multi-scale visualization of pore structures in high-water-content tight gas reservoirs. Therefore, based on the porosity and permeability properties of reservoir cores and the micropore throat structural characteristics, this study designs and prepares three micro-physical models with different permeability ranges. Through micro-experiments and visualization techniques, the microscopic flow phenomena and gas–water distribution in the pore medium are observed. When the water–gas ratio exceeds 5, the produced water type is free water; when the water–gas ratio is between 2 and 5, the produced water type is weak capillary water; and when the water–gas ratio is less than 2, the produced water type is strong capillary water. The latter two types are collectively referred to as capillary water. In the Jin 30 well area, the main types of produced water are first free water, followed by capillary water, accounting for 58.5%. The experimental results of the micro-physical models with different permeability levels show that the production pattern of formation water varies due to differences in pore connectivity. In the low-permeability model, the high proportion of nano-pores and small pore throats requires a large pressure difference to mobilize capillary water, resulting in a higher proportion of residual water. Although the pores in the medium-permeability model are larger, the poor connectivity of nano-pores leads to local water phase retention. In the high-permeability model, micro-fractures and micropores are highly developed with good connectivity, allowing for rapid mobilization of multi-scale water phases under low pressure. The connectivity of nano-pores directly impacts the mobilization of formation water in micron-scale fractures, and poor pore connectivity significantly increases the difficulty of capillary water mobilization, thus changing the production mechanism of formation water at different scales. Full article

We study the relationship between the physical surface roughness of the glossy surfaces of dielectric objects and the roughness parameter in image rendering. The former refers to a measure of the microscopic surface structure of a real object’s surface. The latter is a model parameter used to produce the realistic appearance of objects. The target dielectric objects to analyze the surface roughness are handcrafted lacquer plates with controlled surface glossiness, as well as several plastics and lacquer products from everyday life. We first define the physical surface roughness as the standard deviation of the surface normal, and provide the computational procedure. We use a laser scanning system to obtain the precise surface height information at tiny flat areas of a surface. Next, a method is developed for estimating the surface roughness parameter based on images taken of the surface with a camera. With a simple setup for observing a glossy flat surface, we estimate the roughness parameter by fitting the Beckmann function to the image intensity distribution in the observed HDR image using the least squares method. A linear relationship is then found between the measurement-based surface roughness and image-based surface roughness. We present applications to glossy objects with curved surfaces. Full article

Background/Objectives: Minimally invasive spine surgery (MISS) is complex and requires proficiency with a variety of technological and robotic modalities. Acquiring these skills is a long and involved process, often with a steep learning curve. This paper seeks to characterize the state of MISS training in neurosurgical and orthopedic residency programs, focusing on their effectiveness at minimizing substantial learning curves in the field, as well as highlighting potential areas for future growth. Methods: We conducted a scoping review of the PubMed, Scopus, and Embase databases utilizing the PRISMA extension for scoping reviews. Results: Of the 100 studies initially identified, 16 were included in our final analysis. MISS training types could be broadly grouped into four categories: virtual simulation (including AR and VR), physical models, hybrid didactic and simulation, and mentored training. Training with these modalities led to improvements in resident performance across multiple different MISS techniques, including percutaneous pedicle screw fixation, MIS dural repair, MIS-TLIF, MIS-LLIF, MIS-ULBD, microscopic discectomy/disk herniation repair, percutaneous needle placement, and surgical navigation. Specific improvements included reduced error rate, operation time, and fluoroscopy exposure, as well as increased procedural knowledge, accuracy, and confidence. Conclusions: The incorporation of MISS training modalities in spine surgery residency leads to increases in simulated performance and could serve as a means of overcoming significant learning curves in the field. Full article

Dictyophora indusiata cultivation is severely impeded by premature hyphal regression. This study elucidates the spatiotemporal dynamics of mycelial regression and associated microbial succession in both substrate and soil matrices across progressive regression stages (CK: normal growth; S1: initial recession; S2: advanced recession; S3: complete recession). Microscopic analysis revealed preferential mycelial regression in the substrate, preceding soil regression by 1–2 stages. High-throughput sequencing demonstrated significant fungal community restructuring, characterized by a sharp decline in Phallus abundance (substrate: 99.7% → 7.0%; soil: 78.3% → 5.5%) and concomitant explosive proliferation of Trichoderma (substrate: 0% → 45.2%; soil: 0.1% → 55.3%). Soil fungal communities exhibited a higher richness (Chao1, p < 0.05) and stability, attributed to functional redundancy (e.g., Aspergillus, Conocybe) and physical protection by organic–mineral complexes. Conversely, substrate bacterial diversity dominated, driven by organic matter availability (e.g., the Burkholderia–Caballeronia–Paraburkholderia complex surged to 59%) and optimized porosity. Niche analysis confirmed intensified competition in post-regression soil (niche differentiation) versus substrate niche contraction under Trichoderma dominance. Critically, Trichoderma overgrowing was mechanistically linked to (1) nutrient competition via activated hydrolases (e.g., Chit42) and (2) pathogenic activity (e.g., T. koningii causing rot). We propose ecological control strategies: application of antagonistic Bacillus subtilis (reducing Trichoderma by 63%), substrate C/N ratio modulation via soybean meal amendment, and Sphingomonas–biochar soil remediation. This work provides the first integrated microbial niche model for D. indusiata regression, establishing a foundation for sustainable cultivation. Full article

Non-uniform corrosion cracking in reinforced concrete buildings constitutes a fundamental difficulty resulting in durability failure. This work develops a microscopic-scale multi-species electrochemical phase field model to tackle this issue. The model comprehensively examines the spatiotemporal coupling mechanisms of the full “corrosion-rust swelling-cracking” process by integrating electrochemical reaction kinetics, multi-ion transport processes, and a unified phase field fracture theory. The model uses local corrosion current density as the primary variable to accurately measure the dynamic interactions among electrochemical processes, ion transport, and rust product precipitation. It incorporates phase field method simulations of fracture initiation and propagation in concrete, establishing a bidirectional link between rust swelling stress and crack development. Experimental validation confirms that the model’s predictions about cracking duration, crack shape, and ion concentration distribution align well with empirical data, substantiating the efficacy of local corrosion current density as an indicator of electrochemical reaction rate. Parametric studies were performed to examine the effects of interface transition zone strength, oxygen diffusion coefficient, protective layer thickness, reinforcing bar diameter, and reinforcing bar configuration on cracking patterns. This model’s multi-physics field coupling framework, influenced by dynamic corrosion current density, facilitates cross-field interactions, offering sophisticated theoretical tools and technical support for the quantitative analysis, durability evaluation, and protective design of corrosion-induced cracking in reinforced concrete structures. Full article